






'''simple docstring'''







import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  Any   ):
       """simple docstring"""




       a_						     =			'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
       a_						     =			Image.open(requests.get(lowercase_   ,       stream=lowercase_   ).raw   ).convert('RGB'   )

       a_						     =			transforms.Compose(
           [
               transforms.Resize((image_size, image_size)   ,       interpolation=InterpolationMode.BICUBIC   ),
               transforms.ToTensor(),
               transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)   ,       (0.2686_2954, 0.2613_0258, 0.2757_7711)   ),
           ]   )
       a_						     =			transform(lowercase_   ).unsqueeze(0   ).to(lowercase_   )
       return image


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       if "visual_encoder" in key:
              a_						     =			re.sub('visual_encoder*'   ,       'vision_model.encoder'   ,       lowercase_   )
       if "blocks" in key:
              a_						     =			re.sub(R'blocks'   ,       'layers'   ,       lowercase_   )
       if "attn" in key:
              a_						     =			re.sub(R'attn'   ,       'self_attn'   ,       lowercase_   )
       if "norm1" in key:
              a_						     =			re.sub(R'norm1'   ,       'layer_norm1'   ,       lowercase_   )
       if "norm2" in key:
              a_						     =			re.sub(R'norm2'   ,       'layer_norm2'   ,       lowercase_   )
       if "encoder.norm" in key:
              a_						     =			re.sub(R'encoder.norm'   ,       'post_layernorm'   ,       lowercase_   )
       if "encoder.patch_embed.proj" in key:
              a_						     =			re.sub(R'encoder.patch_embed.proj'   ,       'embeddings.patch_embedding'   ,       lowercase_   )

       if "encoder.pos_embed" in key:
              a_						     =			re.sub(R'encoder.pos_embed'   ,       'embeddings.position_embedding'   ,       lowercase_   )
       if "encoder.cls_token" in key:
              a_						     =			re.sub(R'encoder.cls_token'   ,       'embeddings.class_embedding'   ,       lowercase_   )

       if "self_attn" in key:
              a_						     =			re.sub(R'self_attn.proj'   ,       'self_attn.projection'   ,       lowercase_   )

       return key


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  int   ,       lowercase_       :  int=None   ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			BlipConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			BlipConfig(projection_dim=512   ,       text_config={}   ,       vision_config={}   )

       a_						     =			BlipForConditionalGeneration(lowercase_   ).eval()

       a_						     =			'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

       a_						     =			blip_decoder(pretrained=lowercase_   ,       image_size=384   ,       vit='base'   )
       a_						     =			pt_model.eval()

       a_						     =			pt_model.state_dict()
       for key in modified_state_dict.copy():
              a_						     =			modified_state_dict.pop(lowercase_   )
              a_						     =			rename_key(lowercase_   )
              a_						     =			value

       hf_model.load_state_dict(lowercase_   )

       a_						     =			384
       a_						     =			load_demo_image(image_size=lowercase_   ,       device='cpu'   )
       a_						     =			BertTokenizer.from_pretrained('bert-base-uncased'   )
       a_						     =			tokenizer(['a picture of']   ).input_ids

       a_						     =			hf_model.generate(lowercase_   ,       lowercase_   )

       assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

       a_						     =			hf_model.generate(lowercase_   )

       assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

       if pytorch_dump_folder_path is not None:
              hf_model.save_pretrained(lowercase_   )

       # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
       a_						     =			(
           'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
       )

       a_						     =			blip_vqa(pretrained=lowercase_   ,       image_size=lowercase_   ,       vit='base'   )
       vqa_model.eval()

       a_						     =			vqa_model.state_dict()
       for key in modified_state_dict.copy():
              a_						     =			modified_state_dict.pop(lowercase_   )
              a_						     =			rename_key(lowercase_   )
              a_						     =			value

       a_						     =			BlipForQuestionAnswering(lowercase_   )

       hf_vqa_model.load_state_dict(lowercase_   )

       a_						     =			['How many dogs are in this image?']
       a_						     =			tokenizer(lowercase_   ,       return_tensors='pt'   ).input_ids

       a_						     =			hf_vqa_model.generate(lowercase_   ,       lowercase_   )
       print(tokenizer.decode(answer[0]   )   )

       assert tokenizer.decode(answer[0]   ) == "[UNK] 1 [SEP]"
       if pytorch_dump_folder_path is not None:
              hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'   )

       a_						     =			'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

       a_						     =			blip_itm(pretrained=lowercase_   ,       image_size=lowercase_   ,       vit='base'   )
       itm_model.eval()

       a_						     =			itm_model.state_dict()
       for key in modified_state_dict.copy():
              a_						     =			modified_state_dict.pop(lowercase_   )
              a_						     =			rename_key(lowercase_   )
              a_						     =			value

       a_						     =			BlipForImageTextRetrieval(lowercase_   )

       a_						     =			['A picture of a woman with a dog sitting in a beach']
       a_						     =			tokenizer(
           lowercase_   ,       return_tensors='pt'   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=35   ,       ).input_ids

       hf_itm_model.load_state_dict(lowercase_   )
       hf_itm_model.eval()

       a_						     =			hf_itm_model(lowercase_   ,       lowercase_   ,       use_itm_head=lowercase_   )
       a_						     =			hf_itm_model(lowercase_   ,       lowercase_   ,       use_itm_head=lowercase_   )

       assert out[0].item() == 0.2110_6874_9427_7954
       assert torch.nn.functional.softmax(out_itm[0]   ,       dim=1   )[:, 1].item() == 0.4_5698_8453_8650_5127

       if pytorch_dump_folder_path is not None:
              hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  __lowerCAmelCase        =							parser.parse_args()

  convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)












'''simple docstring'''







import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger(__name__)

__lowerCAmelCase        =							[
    ("bert.bert", "visual_bert"),
    ("bert.cls", "cls"),
    ("bert.classifier", "cls"),
    ("token_type_embeddings_visual", "visual_token_type_embeddings"),
    ("position_embeddings_visual", "visual_position_embeddings"),
    ("projection", "visual_projection"),
]

__lowerCAmelCase        =							[
    "nlvr2_coco_pre_trained.th",
    "nlvr2_fine_tuned.th",
    "nlvr2_pre_trained.th",
    "vcr_coco_pre_train.th",
    "vcr_fine_tune.th",
    "vcr_pre_train.th",
    "vqa_coco_pre_trained.th",
    "vqa_fine_tuned.th",
    "vqa_pre_trained.th",
]


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			torch.load(lowercase_   ,       map_location='cpu'   )
       return sd


def       __UpperCamelCase					(      lowercase_       :  List[str]   ,       lowercase_       :  Tuple   ,       lowercase_       :  Union[str, Any]=rename_keys_prefix   ):
       """simple docstring"""




       a_						     =			OrderedDict()
       a_						     =			torch.arange(config.max_position_embeddings   ).expand((1, -1)   )
       # detector_d = OrderedDict()
       for key in d:
              if "detector" in key:
                     # detector_d[key.replace('detector.','')] = d[key]
                     continue
              a_						     =			key
              for name_pair in rename_keys_prefix:
                     a_						     =			new_key.replace(name_pair[0]   ,       name_pair[1]   )
              a_						     =			d[key]
              if key == "bert.cls.predictions.decoder.weight":
                     # Old bert code didn't have `decoder.bias`, but was added separately
                     a_						     =			new_d['cls.predictions.bias']
       return new_d


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  Union[str, Any]   ):
       """simple docstring"""




       assert (
           checkpoint_path.split('/'   )[-1] in ACCEPTABLE_CHECKPOINTS
       ), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'

       # Get Config
       if "pre" in checkpoint_path:
              a_						     =			'pretraining'
              if "vcr" in checkpoint_path:
                     a_						     =			{'visual_embedding_dim': 512}
              elif "vqa_advanced" in checkpoint_path:
                     a_						     =			{'visual_embedding_dim': 2_048}
              elif "vqa" in checkpoint_path:
                     a_						     =			{'visual_embedding_dim': 2_048}
              elif "nlvr" in checkpoint_path:
                     a_						     =			{'visual_embedding_dim': 1_024}
              else:
                     raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.'   )
       else:
              if "vcr" in checkpoint_path:
                     a_						     =			{'visual_embedding_dim': 512}
                     a_						     =			'multichoice'
              elif "vqa_advanced" in checkpoint_path:
                     a_						     =			{'visual_embedding_dim': 2_048}
                     a_						     =			'vqa_advanced'
              elif "vqa" in checkpoint_path:
                     a_						     =			{'visual_embedding_dim': 2_048, 'num_labels': 3_129}
                     a_						     =			'vqa'
              elif "nlvr" in checkpoint_path:
                     a_						     =			{
                         'visual_embedding_dim': 1_024,
                         'num_labels': 2,
                     }
                     a_						     =			'nlvr'

       a_						     =			VisualBertConfig(**lowercase_   )

       # Load State Dict
       a_						     =			load_state_dict(lowercase_   )

       a_						     =			get_new_dict(lowercase_   ,       lowercase_   )

       if model_type == "pretraining":
              a_						     =			VisualBertForPreTraining(lowercase_   )
       elif model_type == "vqa":
              a_						     =			VisualBertForQuestionAnswering(lowercase_   )
       elif model_type == "nlvr":
              a_						     =			VisualBertForVisualReasoning(lowercase_   )
       elif model_type == "multichoice":
              a_						     =			VisualBertForMultipleChoice(lowercase_   )

       model.load_state_dict(lowercase_   )
       # Save Checkpoints
       Path(lowercase_   ).mkdir(exist_ok=lowercase_   )
       model.save_pretrained(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
  parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
  __lowerCAmelCase        =							parser.parse_args()
  convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)




